Details

Autor(en) / Beteiligte

• Zhu, Qiang
• Hong, Cheng
• Meng, Yajie
• Yang, Huali
• Zhao, Weizhong

Titel

Joint extraction of biomedical overlapping triples through feature partition encoding

Ist Teil von

• Expert systems with applications, 2024-05, Vol.241, p.122723, Article 122723

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2024

Quelle

ScienceDirect

Beschreibungen/Notizen

• Entities and relations extraction are the key tasks in the construction of biomedical knowledge graph, which play an important role in the biomedical artificial intelligence. However, extraction of entities and relations from biomedical texts is challenging because of the overlapping triples problem. The previous approaches typically divided the task into two separate sub-tasks. However, these methods failed to address the error propagation problem. Recent methods have been proposed to perform both sub-tasks simultaneously. Nonetheless, most current methods still encounter issues related to imbalanced interactions and independent features. In this paper, we propose a novel method based on feature partition encoding and relative positional embedding to joint extract biomedical entity and relation triples simultaneously. Compared to previous work, our method shows exceptional accurate in extracting entities and relations, while efficiently tackling the challenge of overlapping triples in biomedical texts. Our work has two contributions. Firstly, our method divides the features into task-specific and shared parts through entity, relation and sharing partitions at the encoding stage. And the encoded features will be aggregated according to the subsequent tasks. Secondly, we introduce a relative positional embedding method to capture the relative distance information between token pairs. In this way, our method can effectively deal with the sub-tasks interactions problem and improve entities and relations extraction. The experimental results show that our method improves the F1 scores of relations extraction by 3.2%, 2.1%, 3.4%, and 2.8% on four biomedical datasets, respectively. •Joint extraction is to recognize biomedical entities and relations simultaneously.•Feature partition encoding divides neurons into entity, relation, shared partition.•Relative positional embedding benefits both entities and relations extraction.•FPE is effective in addressing the issue of overlapping triples in biomedicine.